This course for Microsoft Azure machine learning teaches you to use open-source tools and frameworks to create and publish machine learning models with no-code techniques. You will learn to use Azure tools to handle complex machine learning tasks and gain hands-on practical experience. By the end of the program, you will have deeper technical skills to develop intricate machine learning models and aim for higher-level positions. The course is available in a self-paced learning option, online boot camp, or customized corporate training. To join this course, you should have knowledge of basic Python programming concepts, algebra, fundamental statistics, fundamental machine learning concepts, and an understanding of Azure basics.
Prerequisites
Before starting this advanced-level course, it is recommended that you have a basic understanding of Python programming concepts, algebra, fundamental statistics, fundamental machine learning concepts, and an understanding of Azure basics to ensure that you can keep pace with the course content. If you need to brush up on these skills, we recommend checking out our machine learning course online.
What will you gain after this course
Upon completion of this Microsoft Azure Machine Learning course, you will be able to:
Use open-source tools and frameworks to create and publish machine learning models with no-code techniques.
Use Azure tools to handle complex machine learning tasks.
Develop advanced technical skills to create intricate machine learning models.
Acquire knowledge of automated machine learning, Azure Machine Learning Designer, create a regression model, classification model with Azure AI, and clustering model with Azure AI.
Prepare for high-level positions in machine learning engineering.
Receive a certificate upon completion of the course.
Jobs you can get with a Microsoft Azure Machine Learning Certification
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Who is this certification for?
The Microsoft Azure Machine Learning course is designed for individuals who are interested in pursuing a career in machine learning or those who want to switch careers to the machine learning field. Whether you are a student or a working professional, this training program can help you acquire the necessary skills to create and publish machine learning models using open-source tools and frameworks with no-code techniques.
Due to the in-depth focus on technical machine learning methods, and the broad scope of this course, those with current machine learning knowledge, data science aspirers, and/or experienced programmers will benefit the most from this course, including:
Machine Learning Engineer
Data Scientist
Software Developer
Data Analyst
Small Groups
With small groups of students, our instructors can work closely with each student.
Flexible Class Schedules
Our class schedules are flexible on weekdays, weekend, or evenings to suit your schedule.
Experienced Instructors
Our instructors follow a modified are personalized approach to engage students during class
Hi-Tech Lab Facilities
Our students can access our lab facilities anytime for practical experience during and after studies.
Syllabus
I. Supervised Learning
Regression
Learn the difference between Regression and Classification
Train a Linear Regression model to predict values
Predict states using Logistic Regression
Perceptron Algorithms
Learn the definition of a perceptron as a building block for neural networks, and the perceptron algorithm for classification
Decision Trees
Train Decision Trees to predict states
Use Entropy to build decision trees, recursively
Naive Bayes’
Learn Bayes’ rule, and apply it to predict cases of spam messages using the Naive Bayes algorithm
Train models using Bayesian Learning
Complete an exercise that uses Bayesian Learning for natural language processing
Support Vector Machines
Train a Support Vector Machines to separate data, linearly
Use Kernel Methods in order to train SVMs on data that is not linearly separable
Ensemble of Learners
Build data visualizations for quantitative and categorical data
Create pie, bar, line, scatter, histogram, and boxplot charts
Build professional presentations
Evaluation Metrics
Learn about different metrics to measure model success
Calculate accuracy, precision, and recall to measure the performance of your models
Training and Tuning Models
Train and test models with Scikit-learn
Choose the best model using evaluation techniques like cross-validation and grid search
II. Neural Networks
Introduction to Neural Networks
Learn the foundations of deep learning and neural networks
Implement gradient descent and backpropagation in Python
Implementing Gradient Descent
Implement gradient descent using NumPy matrix multiplication
Training Neural Networks
Learn several techniques to effectively train a neural network
Prevent overfitting of training data and learn best practices for minimizing the error of a network
Deep Learning with PyTorch
Learn how to use PyTorch for building deep learning models
III. Unsupervised Learning
Clustering
Learn the basics of clustering data
Cluster data with the K-means algorithm
Hierarchical and Density-Based Clustering
Cluster data with Single Linkage Clustering
Cluster data with DBSCAN, a clustering method that captures the insight that clusters are dense group of points
Gaussian Mixture Models
Cluster data with Gaussian Mixture Models
Optimize Gaussian Mixture Models with and Expectation Maximization
Dimensionality Reduction
Reduce the dimensionality of the data using Principal
Component Analysis and Independent Component Analysis
Yes, Azure is a powerful platform for managing and utilizing big data to build advanced analytics solutions. It provides an easy and flexible interface for machine learning engineers and enables easy documentation maintenance for machine learning solutions.
Microsoft Azure Machine Learning is a cloud-based service platform that helps data scientists, machine learning professionals, and engineers to accelerate and manage the machine learning project lifecycle. It is used to manage MLOps by training and deploying models in their daily workflows.
While it is not necessary to have programming knowledge to learn Microsoft Azure for machine learning, you may need to write a small deployment script or configuration code in situations where you need to deploy an application to Azure. Furthermore, Microsoft Azure can be easily used for infrastructure management activities and other tasks.
Azure Machine Learning helps in the processing of large data projects and gives you predictive arrangements of information. It saves time for machine learning engineers and also minimizes the coding requirements. It allows you to use drag and drop features to create experiments and publish data models in less time. Therefore, learning Microsoft Azure for machine learning is an added advantage.
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Certification
Microsoft Azure